Abstract
Epilepsy is a neurological disorder that affects people of any age, which can be detected by Electroencephalogram (EEG) signals. This paper proposes a novel method called Volume of Phase Space Representation (VOPSR) to classify seizure and seizure-free EEG signals automatically. Primarily, the recorded EEG signal is disintegrated into several Intrinsic Mode Functions (IMFs) using the Empirical Mode Decomposition (EMD) method and the three-dimensional phase space have been reconstructed for the obtained IMFs. The volume is measured for the obtained 3D-PSR for different IMFs called VOPSR, which is used as a feature set for the classification of Epileptic seizure EEG signals. Support vector machine (SVM) is used as a classifier for the classification of epileptic and epileptic-free EEG signals. The classification performance of the proposed method is evaluated under different kernels such as Linear, Polynomial and Radial Basis Function (RBF) kernels. Finally, the proposed method outperforms noteworthy state-of-the-art classification methods in the context of epileptic EEG signals, achieving 99.13% accuracy (average) with the Linear, Polynomial, and RBF kernels. The proposed technique can be used to detect epilepsy from the EEG signals automatically without human intervention.
Similar content being viewed by others
References
Mormann F, Andrzejak RG, Elger CE, Lehnertz K (2007) Seizure prediction: the long and winding road. Brain 130(2):314–333
Ray GC (1994) An algorithm to separate non stationary part of a signal using mid-prediction filter. IEEE Trans Signal Process 42(9):2276–2279
Mukhopadhyay S, Ray GC (1998) A new interpretation of nonlinear energy operator and its efficacy in spike detection. IEEE Trans Biomed Eng 45(2):180–187
Iasemidis LD et al (2003) Adaptive epileptic seizure prediction system. IEEE Trans Biomed Eng 50(5):616–627
Acharya UR, Sree SV, Swapna G, Martis RJ, Suri JS (2013) Automated EEG analysis of epilepsy: a review. Knowl-Based Syst 45:147–165
Altunay S, Telatar Z, Erogul O (2010) Epileptic EEG detection using the linear prediction error energy. Expert Syst Appl 37(8):5661–5665
Joshi V, Pachori RB, Vijesh A (2014) Classification of ictal and seizure-free EEG signals using fractional linear prediction. Biomed Signal Process Control 9:1–5
Srinivasan V, Eswaran C, Sriraam N (2005) Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst 29(6):647–660
Polat K, Günes S (2007) Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform. Appl Math Comput 187(2):1017–1026
Tzallas AT, Tsipouras MG, Fotidis DI (2007) Automatic seizure detection based on time–frequency analysis and artificial neural networks. Comput Intell Neurosci 2007:13. Article ID 80510
Tzallas AT, Tsipouras MG, Fotiadis DI (2009) Epileptic seizure detection in EEGs using time–frequency analysis. IEEE Trans Inf Technol Biomed 13(5):703–710
Guo L, Rivero D, Pazos A (2010) Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J Neurosci Methods 193(1):156–163
Adeli H, Zhou Z, Dadmehr N (2003) Analysis of EEG records in an epileptic patient using wavelet transform. J Neurosci Methods 123(1):69–87
Khan YU, Gotman J (2003) Wavelet based automatic seizure detection in intracerebral electroencephalogram. Clin Neurophysiol 114(5):898–908
Ghosh-Dastidar S, Adeli H, Dadmehr N (2007) Mixed-band wavelet-chaos-neural network methodology for epilepsy and epileptic seizure detection. IEEE Trans Biomed Eng 54(9):1545–1551
Ocak H (2009) Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Syst Appl 36(2):2027–2036
Adeli H, Ghosh-Dastidar S, Dadmehr N (2007) A wavelet-chaos methodology for analysis of EEGs and EEG subbands to detect seizure and epilepsy. IEEE Trans Biomed Eng 54(2):205–211
Subasi A (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Syst Appl 32(4):1084–1093
Uthayakumar R, Easwaramoorthy D (2013) Epileptic seizure detection in EEG signals using multifractal analysis and wavelet transform. Fractals 21(2):1350011
Lee S-H, Lim JS, Kim J-K, Yang J, Lee Y (2014) Classification of normal and epileptic seizure EEG signals using wavelet transform, phase-space reconstruction, and Euclidean distance. Comput Methods Programs Biomed 116:10–25
Güler NF, Übeyli ED, Güler I (2005) Recurrent neural networks employing Lyapunov exponents for EEG signal classification. Expert Syst Appl 29(3):506–514
Übeyli ED (2010) Lyapunov exponents/probabilistic neural networks for analysis of EEG signals. Expert Syst Appl 37(2):985–992
Lehnertz K, Elger CE (1995) Spatio-temporal dynamics of the primary epileptogenic area in temporal lobe epilepsy characterized by neuronal complexity loss. Electroencephalogr Clin Neurophysiol 95(2):108–117
Accardo A, Affinito M, Carrozzi M, Bouquet F (1997) Use of the fractal dimension for the analysis of electroencephalographic time series. Biol Cybern 77(5):339–350
Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288–295
Liang SF, Wang HC, Chang WL (2010) Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 2010:15. Article ID 853434
Liang S-F, Wang H-C, Chang W-L (2010) Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 2010:853434
Pachori RB (2008) Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res Lett Signal Process 2008:5. Article ID 293056
Oweis RJ, Abdulhay EW (2011) Seizure classification in EEG signals utilizing Hilbert–Huang transform. Bio Med Eng Online 10:38
Pachori RB, Bajaj V (2011) Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. Comput Methods Programs Biomed 104(3):373–381
Bajaj V, Pachori RB (2011) EEG signal classification using empirical mode decomposition and support vector machine. In: Proceedings international conference on soft computing for problem solving, AISC 131, 20–22 December, 2011, Roorkee, India, pp 623–635
Li S et al (2013) Feature extraction and recognition of ictal EEG using EMD and SVM. Comput Biol Med 43(7):807–816
Bajaj V, Pachori RB (2013) Epileptic seizure detection based on the instantaneous area of analytic intrinsic mode functions of EEG signals. Biomed Eng Lett 3(1):17–21
Bajaj V, Pachori RB (2012) Classification of seizure and non- seizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed 16(6):1135–1142
Ur Rehman N, Xia Y, Mandic DP (2010) Application of multivaritate empirical mode decomposition for seizure detection in EEG signals. In: Proceedings annual international conference of IEEE Engineering in Medicine and Biology Society, August 31, 2010–September 04, 2010, Buenos Aires, Argentina, pp 1650–1653
Pachori RB, Patidar S (2014) Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions. Comput Methods Programs Biomed 113:494–502
Pachori RB, Sharma R (2015) Classification of epileptic seizures in EEG signals based on phase space representation of intrinsic mode functions. Expert Syst Appl 42:1106–1117
Bhati D, Pachori RB, Gadre VM (2017) A novel approach for time-frequency localization of scaling functions and design of three-band biorthogonal linear phase wavelet filter banks. Digit Signal Process 69:302–322
Bhati D, Sharma M, Pachori RB, Gadre VM (2017) Time-frequency localized three-band biorthogonal wavelet filter bank using semidefinite relaxation and nonlinear least squares with epileptic seizure EEG signal classification. Digit Signal Process 62:259–273
Bhati D, Pachori RB, Sharma M, Gadre VM (2019) Automated detection of seizure and nonseizure EEG signals using two band biorthogonal wavelet filter banks. In: Naik GR (ed) Biomedical signal processing-advances in theory, algorithms and applications. Springer, Singapore, pp 137–155. https://doi.org/10.1007/978-981-13-9097-5_7
Bhati D, Pachori RB, Gadre VM (2018) Design of time-frequency localized two-band orthogonal wavelet filter banks. Circuits Syst Signal Process 37(08):3295–3312
Huang NE et al (1998) The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary timeseries analysis. Proc R Soc Lond A 454:903–995
Takens F (1981) Detecting strange attractors in turbulence. In: Rand D, Young L-S (eds) Dynamical systemsand turbulence, Warwick 1980. Lecture notes in mathematics. Springer, pp 366–381
Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of non-linear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys Rev E 64:061907
Nicolaou N, Georgiou J (2012) Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst Appl 39:202–209
Zhu G, Li Y, Wen PP (2014) Epileptic seizure detection in EEGs signals using a fast weighted horizontal visibility algorithm. Comput Methods Programs Biomed 115:64–75
Siuly, Li Y, Wen P (2011) Clustering technique-based least square support vector machine for EEG signal classification. Comput Methods Programs Biomed 104(3):358–372
Bao FS, Lie DYC, Zhang Y (2008) A new approach to automated epileptic diagnosis using EEG and probabilistic neural network. In: Proceedings of IEEE international conference on tools with artificial intelligence, pp 482–486
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Krishnaprasanna, R., Vijaya Baskar, V. & Panneerselvam, J. Automatic identification of epileptic seizures using volume of phase space representation. Phys Eng Sci Med 44, 545–556 (2021). https://doi.org/10.1007/s13246-021-01006-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-021-01006-1